The SURVEYLOGISTIC Procedure
- Overview
- Getting Started
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Syntax
PROC SURVEYLOGISTIC StatementBY StatementCLASS StatementCLUSTER StatementCONTRAST StatementDOMAIN StatementEFFECT StatementESTIMATE StatementFREQ StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementOUTPUT StatementREPWEIGHTS StatementSLICE StatementSTORE StatementSTRATA StatementTEST StatementUNITS StatementWEIGHT Statement -
DetailsMissing ValuesModel SpecificationModel FittingSurvey Design InformationLogistic Regression Models and ParametersVariance EstimationDomain AnalysisHypothesis Testing and EstimationLinear Predictor, Predicted Probability, and Confidence LimitsOutput Data SetsDisplayed OutputODS Table NamesODS Graphics
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Examples
- References
Iterative Algorithms for Model Fitting
Two iterative maximum likelihood algorithms are available in PROC SURVEYLOGISTIC to obtain the pseudo-estimate 
of the model parameter 
. The default is the Fisher scoring method, which is equivalent to fitting by iteratively reweighted least squares. The alternative
algorithm is the Newton-Raphson method. Both algorithms give the same parameter estimates; the covariance matrix of is estimated in the section Variance Estimation. For a generalized logit model, only the Newton-Raphson technique is available. You can use the TECHNIQUE= option in the MODEL statement to select a fitting algorithm.